- R generation
https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x
1 R yükleme
http://www.youtube.com/watch?v=XcBLEVknqvY
1.2 RStudio
https://www.rstudio.com/products/rstudio/download/
https://moderndive.com/2-getting-started.html
1.2.1 RStudio eklentileri
- Discover and install useful RStudio addins
https://cran.r-project.org/web/packages/addinslist/README.html
https://rstudio.github.io/rstudioaddins/
2 R zor şeyler için kolay, kolay şeyler için zor
Aynı şeyi çok fazla şekilde yapmak mümkün
R Syntax Comparison::CHEAT SHEET
https://www.amelia.mn/Syntax-cheatsheet.pdf
3 R paketleri
3.1 Neden paketler var
I love the #rstats community.
— Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) July 3, 2018
Someone is like, “oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It's called pbplyr.”
What a tribe.
https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/
3.2 Paketleri nereden bulabiliriz
Available CRAN Packages By Name
https://cran.r-project.org/web/packages/available_packages_by_name.htmlCRAN Task Views
https://cran.r-project.org/web/views/Bioconductor
https://www.bioconductor.orgRecommendR
http://recommendr.info/pkgsearch
CRAN package search
https://github.com/metacran/pkgsearchCRANsearcher
https://github.com/RhoInc/CRANsearcherAwesome R
https://awesome-r.com/
3.3 Kendi paket evrenini oluştur
- pkgverse: Build a Meta-Package Universe
https://cran.r-project.org/web/packages/pkgverse/index.html
3.4 R için yardım bulma
- Vignette
RDocumentation https://www.rdocumentation.org
R Package Documentation https://rdrr.io/
GitHub
Stackoverflow
- Google uygun anahtar kelime
How I use #rstats
— Emily Bovee (@ebovee09) August 10, 2018
h/t @ThePracticalDev pic.twitter.com/erRnTG0Ujr
- Awesome Cheatsheet https://github.com/detailyang/awesome-cheatsheet
http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf
https://www.rstudio.com/resources/cheatsheets/
- Awesome R
https://github.com/qinwf/awesome-R#readme
https://twitter.com/hashtag/rstats?src=hash
- Reproducible Examples
Got a question to ask on @SlackHQ or post on @github? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by @JennyBryan) #rstat pic.twitter.com/gpuGXpFIsX
— ZhiYang (@zhiiiyang) October 18, 2018
- Keeping up to date with R news
https://masalmon.eu/2019/01/25/uptodate/
3.5 R paket yükleme
install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")# require(tidyverse)
# require(jmv)
# require(questionr)
# library(summarytools)
# library(gganimate)4 R studio ile proje oluşturma
https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects
5 RStudio ile veri yükleme
https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio
5.1 Excel
5.2 SPSS
5.3 csv
6 Veriyi görüntüleme
Spreadsheet users using #rstats: where's the data?#rstats users using spreadsheets: where's the code?
— Leonard Kiefer (@lenkiefer) July 7, 2018
View(data)dataheadtailglimpsestrskimr::skim()7 Veriyi değiştirme
7.1 Veriyi kod ile değiştirelim
7.2 Veriyi eklentilerle değiştirme
7.3 RStudio aracılığıyla recode
questionr paketi kullanılacak
https://juba.github.io/questionr/articles/recoding_addins.html
8 Basit tanımlayıcı istatistikler
summary()meanmedianminmaxsdtable()Parsed with column specification:
cols(
Sepal.Length = [32mcol_double()[39m,
Sepal.Width = [32mcol_double()[39m,
Petal.Length = [32mcol_double()[39m,
Petal.Width = [32mcol_double()[39m,
Species = [31mcol_character()[39m
)jmv::descriptives(
data = irisdata,
vars = "Sepal.Length",
splitBy = "Species",
freq = TRUE,
hist = TRUE,
dens = TRUE,
bar = TRUE,
box = TRUE,
violin = TRUE,
dot = TRUE,
mode = TRUE,
sum = TRUE,
sd = TRUE,
variance = TRUE,
range = TRUE,
se = TRUE,
skew = TRUE,
kurt = TRUE,
quart = TRUE,
pcEqGr = TRUE)
DESCRIPTIVES
Descriptives
─────────────────────────────────────────────────────
Species Sepal.Length
─────────────────────────────────────────────────────
N setosa 50
versicolor 50
virginica 50
Missing setosa 0
versicolor 0
virginica 0
Mean setosa 5.01
versicolor 5.94
virginica 6.59
Std. error mean setosa 0.0498
versicolor 0.0730
virginica 0.0899
Median setosa 5.00
versicolor 5.90
virginica 6.50
Mode setosa 5.00
versicolor 5.50
virginica 6.30
Sum setosa 250
versicolor 297
virginica 329
Standard deviation setosa 0.352
versicolor 0.516
virginica 0.636
Variance setosa 0.124
versicolor 0.266
virginica 0.404
Range setosa 1.50
versicolor 2.10
virginica 3.00
Minimum setosa 4.30
versicolor 4.90
virginica 4.90
Maximum setosa 5.80
versicolor 7.00
virginica 7.90
Skewness setosa 0.120
versicolor 0.105
virginica 0.118
Std. error skewness setosa 0.337
versicolor 0.337
virginica 0.337
Kurtosis setosa -0.253
versicolor -0.533
virginica 0.0329
Std. error kurtosis setosa 0.662
versicolor 0.662
virginica 0.662
25th percentile setosa 4.80
versicolor 5.60
virginica 6.23
50th percentile setosa 5.00
versicolor 5.90
virginica 6.50
75th percentile setosa 5.20
versicolor 6.30
virginica 6.90
───────────────────────────────────────────────────── 
# install.packages("scatr")
scatr::scat(
data = irisdata,
x = "Sepal.Length",
y = "Sepal.Width",
group = "Species",
marg = "dens",
line = "linear",
se = TRUE)
8.1 summarytools
https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html
8.1.1 Frequencies
Variable: iris$Species
Type: Factor (unordered)
| Freq | % Valid | % Valid Cum. | % Total | % Total Cum. | |
|---|---|---|---|---|---|
| setosa | 50 | 33.33 | 33.33 | 33.33 | 33.33 |
| versicolor | 50 | 33.33 | 66.67 | 33.33 | 66.67 |
| virginica | 50 | 33.33 | 100.00 | 33.33 | 100.00 |
| <NA> | 0 | 0.00 | 100.00 | ||
| Total | 150 | 100.00 | 100.00 | 100.00 | 100.00 |
| Freq | % | % Cum. | |
|---|---|---|---|
| setosa | 50 | 33.33 | 33.33 |
| versicolor | 50 | 33.33 | 66.67 |
| virginica | 50 | 33.33 | 100.00 |
| Total | 150 | 100.00 | 100.00 |
Cross-Tabulation / Row Proportions
Variables: smoker * diseasedData Frame: tobacco
| diseased | |||
|---|---|---|---|
| smoker | Yes | No | Total |
| Yes | 125 (41.95%) | 173 (58.05%) | 298 (100.00%) |
| No | 99 (14.10%) | 603 (85.90%) | 702 (100.00%) |
| Total | 224 (22.40%) | 776 (77.60%) | 1000 (100.00%) |
Generated by summarytools 0.8.8 (R version 3.5.1)
2019-01-01
with(tobacco,
print(ctable(smoker, diseased, prop = 'n', totals = FALSE),
omit.headings = TRUE, method = "render"))| diseased | ||
|---|---|---|
| smoker | Yes | No |
| Yes | 125 | 173 |
| No | 99 | 603 |
Generated by summarytools 0.8.8 (R version 3.5.1)
2019-01-01
Non-numerical variable(s) ignored: Species ### Descriptive Statistics
Data Frame: iris
N: 150
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | |
|---|---|---|---|---|
| Mean | 5.84 | 3.06 | 3.76 | 1.20 |
| Std.Dev | 0.83 | 0.44 | 1.77 | 0.76 |
| Min | 4.30 | 2.00 | 1.00 | 0.10 |
| Q1 | 5.10 | 2.80 | 1.60 | 0.30 |
| Median | 5.80 | 3.00 | 4.35 | 1.30 |
| Q3 | 6.40 | 3.30 | 5.10 | 1.80 |
| Max | 7.90 | 4.40 | 6.90 | 2.50 |
| MAD | 1.04 | 0.44 | 1.85 | 1.04 |
| IQR | 1.30 | 0.50 | 3.50 | 1.50 |
| CV | 0.14 | 0.14 | 0.47 | 0.64 |
| Skewness | 0.31 | 0.31 | -0.27 | -0.10 |
| SE.Skewness | 0.20 | 0.20 | 0.20 | 0.20 |
| Kurtosis | -0.61 | 0.14 | -1.42 | -1.36 |
| N.Valid | 150.00 | 150.00 | 150.00 | 150.00 |
| Pct.Valid | 100.00 | 100.00 | 100.00 | 100.00 |
descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE,
omit.headings = TRUE, style = "rmarkdown")Non-numerical variable(s) ignored: Species
| Mean | Std.Dev | Min | Median | Max | |
|---|---|---|---|---|---|
| Sepal.Length | 5.84 | 0.83 | 4.30 | 5.80 | 7.90 |
| Sepal.Width | 3.06 | 0.44 | 2.00 | 3.00 | 4.40 |
| Petal.Length | 3.76 | 1.77 | 1.00 | 4.35 | 6.90 |
| Petal.Width | 1.20 | 0.76 | 0.10 | 1.30 | 2.50 |
8.1.2 Data Frame Summary
tobacco
N: 1000
| No | Variable | Stats / Values | Freqs (% of Valid) | Text Graph | Valid | Missing |
|---|---|---|---|---|---|---|
| 1 | gender [factor] |
1. F 2. M |
489 (50.0%) 489 (50.0%) |
IIIIIIIIIIIIIIII IIIIIIIIIIIIIIII |
978 (97.8%) |
22 (2.2%) |
| 2 | age [numeric] |
mean (sd) : 49.6 (18.29) min < med < max : 18 < 50 < 80 IQR (CV) : 32 (0.37) |
63 distinct values | 975 (97.5%) |
25 (2.5%) |
|
| 3 | age.gr [factor] |
1. 18-34 2. 35-50 3. 51-70 4. 71 + |
258 (26.5%) 241 (24.7%) 317 (32.5%) 159 (16.3%) |
IIIIIIIIIIIII IIIIIIIIIIII IIIIIIIIIIIIIIII IIIIIIII |
975 (97.5%) |
25 (2.5%) |
| 4 | BMI [numeric] |
mean (sd) : 25.73 (4.49) min < med < max : 8.83 < 25.62 < 39.44 IQR (CV) : 5.72 (0.17) |
974 distinct values | 974 (97.4%) |
26 (2.6%) |
|
| 5 | smoker [factor] |
1. Yes 2. No |
298 (29.8%) 702 (70.2%) |
IIIIII IIIIIIIIIIIIIIII |
1000 (100%) |
0 (0%) |
| 6 | cigs.per.day [numeric] |
mean (sd) : 6.78 (11.88) min < med < max : 0 < 0 < 40 IQR (CV) : 11 (1.75) |
37 distinct values | 965 (96.5%) |
35 (3.5%) |
|
| 7 | diseased [factor] |
1. Yes 2. No |
224 (22.4%) 776 (77.6%) |
IIII IIIIIIIIIIIIIIII |
1000 (100%) |
0 (0%) |
| 8 | disease [character] |
1. Hypertension 2. Cancer 3. Cholesterol 4. Heart 5. Pulmonary 6. Musculoskeletal 7. Diabetes 8. Hearing 9. Digestive 10. Hypotension [ 3 others ] |
36 (16.2%) 34 (15.3%) 21 ( 9.5%) 20 ( 9.0%) 20 ( 9.0%) 19 ( 8.6%) 14 ( 6.3%) 14 ( 6.3%) 12 ( 5.4%) 11 ( 5.0%) 21 ( 9.5%) |
IIIIIIIIIIIIIIII IIIIIIIIIIIIIII IIIIIIIII IIIIIIII IIIIIIII IIIIIIII IIIIII IIIIII IIIII IIII IIIIIIIII |
222 (22.2%) |
778 (77.8%) |
| 9 | samp.wgts [numeric] |
mean (sd) : 1 (0.08) min < med < max : 0.86 < 1.04 < 1.06 IQR (CV) : 0.19 (0.08) |
0.86!: 267 (26.7%) 1.04!: 249 (24.9%) 1.05!: 324 (32.4%) 1.06!: 160 (16.0%) ! rounded |
IIIIIIIIIIIII IIIIIIIIIIII IIIIIIIIIIIIIIII IIIIIII |
1000 (100%) |
0 (0%) |
8.1.3 Descriptive Statistics
Data Frame: iris
Group: Species = setosa
N: 50
| Mean | Std.Dev | Min | Median | Max | |
|---|---|---|---|---|---|
| Sepal.Length | 5.01 | 0.35 | 4.30 | 5.00 | 5.80 |
| Sepal.Width | 3.43 | 0.38 | 2.30 | 3.40 | 4.40 |
| Petal.Length | 1.46 | 0.17 | 1.00 | 1.50 | 1.90 |
| Petal.Width | 0.25 | 0.11 | 0.10 | 0.20 | 0.60 |
Group: Species = versicolor
N: 50
| Mean | Std.Dev | Min | Median | Max | |
|---|---|---|---|---|---|
| Sepal.Length | 5.94 | 0.52 | 4.90 | 5.90 | 7.00 |
| Sepal.Width | 2.77 | 0.31 | 2.00 | 2.80 | 3.40 |
| Petal.Length | 4.26 | 0.47 | 3.00 | 4.35 | 5.10 |
| Petal.Width | 1.33 | 0.20 | 1.00 | 1.30 | 1.80 |
Group: Species = virginica
N: 50
| Mean | Std.Dev | Min | Median | Max | |
|---|---|---|---|---|---|
| Sepal.Length | 6.59 | 0.64 | 4.90 | 6.50 | 7.90 |
| Sepal.Width | 2.97 | 0.32 | 2.20 | 3.00 | 3.80 |
| Petal.Length | 5.55 | 0.55 | 4.50 | 5.55 | 6.90 |
| Petal.Width | 2.03 | 0.27 | 1.40 | 2.00 | 2.50 |
Output file written: /var/folders/76/rq_s_23s7fd5r8hqrbg8rmnc0000gp/T//Rtmpm1NGZD/file6847650f974f.html
Group: Species = versicolor
N: 50
N: 50
| Mean | Std.Dev | Min | Median | Max | |
|---|---|---|---|---|---|
| Sepal.Length | 5.94 | 0.52 | 4.90 | 5.90 | 7.00 |
| Sepal.Width | 2.77 | 0.31 | 2.00 | 2.80 | 3.40 |
| Petal.Length | 4.26 | 0.47 | 3.00 | 4.35 | 5.10 |
| Petal.Width | 1.33 | 0.20 | 1.00 | 1.30 | 1.80 |
Group: Species = virginica
N: 50
Output file appended: /var/folders/76/rq_s_23s7fd5r8hqrbg8rmnc0000gp/T//Rtmpm1NGZD/file6847650f974f.html
N: 50
| Mean | Std.Dev | Min | Median | Max | |
|---|---|---|---|---|---|
| Sepal.Length | 6.59 | 0.64 | 4.90 | 6.50 | 7.90 |
| Sepal.Width | 2.97 | 0.32 | 2.20 | 3.00 | 3.80 |
| Petal.Length | 5.55 | 0.55 | 4.50 | 5.55 | 6.90 |
| Petal.Width | 2.03 | 0.27 | 1.40 | 2.00 | 2.50 |
Generated by summarytools 0.8.8 (R version 3.5.1)
2019-01-01
8.1.4 Descriptive Statistics
Variable: tobacco$BMI by age.gr
| 18-34 | 35-50 | 51-70 | 71 + | |
|---|---|---|---|---|
| Mean | 23.84 | 25.11 | 26.91 | 27.45 |
| Std.Dev | 4.23 | 4.34 | 4.26 | 4.37 |
| Min | 8.83 | 10.35 | 9.01 | 16.36 |
| Median | 24.04 | 25.11 | 26.77 | 27.52 |
| Max | 34.84 | 39.44 | 39.21 | 38.37 |
BMI_by_age <- with(tobacco,
by(BMI, age.gr, descr, transpose = TRUE,
stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)| Mean | Std.Dev | Min | Median | Max | |
|---|---|---|---|---|---|
| 18-34 | 23.84 | 4.23 | 8.83 | 24.04 | 34.84 |
| 35-50 | 25.11 | 4.34 | 10.35 | 25.11 | 39.44 |
| 51-70 | 26.91 | 4.26 | 9.01 | 26.77 | 39.21 |
| 71 + | 27.45 | 4.37 | 16.36 | 27.52 | 38.37 |
tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)
# view(freq_tables, footnote = NA, file = 'freq-tables.html')function ‘is’ appears not to be S3 generic; found functions that look like S3 methods‘>=’ not meaningful for factors$properties
$attributes.lengths names class row.names 5 1 150
$extensive.is [1] “is.data.frame” “is.list”
[3] “is.object” “is.recursive” [5] “is.unsorted”
### Frequencies
**Variable:** tobacco$gender
**Type:** Factor (unordered)
| | Freq | % Valid | % Valid Cum. | % Total | % Total Cum. |
|-----------:|-----:|--------:|-------------:|--------:|-------------:|
| **F** | 489 | 50.00 | 50.00 | 48.90 | 48.90 |
| **M** | 489 | 50.00 | 100.00 | 48.90 | 97.80 |
| **\<NA\>** | 22 | | | 2.20 | 100.00 |
| **Total** | 1000 | 100.00 | 100.00 | 100.00 | 100.00 |Frequencies
Variable: genderType: Factor (unordered)
| Valid | Total | ||||
|---|---|---|---|---|---|
| gender | Freq | % | % Cumul | % | % Cumul |
| F | 489 | 50.00 | 50.00 | 48.90 | 48.90 |
| M | 489 | 50.00 | 100.00 | 48.90 | 97.80 |
| <NA> | 22 | 2.20 | 100.00 | ||
| Total | 1000 | 100.00 | 100.00 | 100.00 | 100.00 |
Generated by summarytools 0.8.8 (R version 3.5.1)
2019-01-01
8.2 skimr
library(skimr)
skim(df)8.3 DataExplorer
library(DataExplorer)
DataExplorer::create_report(df)8.4 Grafikler
### Cross-Tabulation / Row Proportions
**Variables:** gender * smoker
**Data Frame:** tobacco
| | | | | |
|-------:|-------:|-------------:|-------------:|---------------:|
| | smoker | Yes | No | Total |
| gender | | | | |
| F | | 147 (30.06%) | 342 (69.94%) | 489 (100.00%) |
| M | | 143 (29.24%) | 346 (70.76%) | 489 (100.00%) |
| \<NA\> | | 8 (36.36%) | 14 (63.64%) | 22 (100.00%) |
| Total | | 298 (29.80%) | 702 (70.20%) | 1000 (100.00%) |Cross-Tabulation / Row Proportions
Variables: gender * smokerData Frame: tobacco
| smoker | |||
|---|---|---|---|
| gender | Yes | No | Total |
| F | 147 (30.06%) | 342 (69.94%) | 489 (100.00%) |
| M | 143 (29.24%) | 346 (70.76%) | 489 (100.00%) |
| <NA> | 8 (36.36%) | 14 (63.64%) | 22 (100.00%) |
| Total | 298 (29.80%) | 702 (70.20%) | 1000 (100.00%) |
Generated by summarytools 0.8.8 (R version 3.5.1)
2019-01-01
descr(tobacco, style = 'rmarkdown')
print(descr(tobacco), method = 'render', table.classes = 'st-small')
dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)
print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')Here, building up a #ggplot2 as slowly as possible, #rstats. Incremental adjustments. #rstatsteachingideas pic.twitter.com/nUulQl8bPh
— Gina Reynolds (@EvaMaeRey) August 13, 2018
Dreaming of a fancy #Rstats #ggplot #dataviz but still scared of typing #code? @_pvictorr esquisse package has you covered https://t.co/1vIDXcVAAF pic.twitter.com/RlTkptnrNv
— Radoslaw Panczak (@RPanczak) October 2, 2018
9 Rcmdr
library(Rcmdr)
Rcmdr::Commander()
- A Comparative Review of the R Commander GUI for R
http://r4stats.com/articles/software-reviews/r-commander/
11 Sonraki Konular
- RStudio ile GitHub
- Hipotez testleri
- R Markdown ve R Notebook ile tekrarlanabilir rapor
Bu bir derlemedir, mümkün mertebe alıntılara referans vermeye çalıştım.↩
---
title: R ile analize başlarken^[Bu bir derlemedir, mümkün mertebe alıntılara referans
  vermeye çalıştım.]
author: "Derleyen [Serdar Balcı, MD, Pathologist](https://sbalci.github.io/)"
date: "`r format(Sys.Date())`"
output:
  html_notebook:
    fig_caption: yes
    highlight: kate
    number_sections: yes
    theme: flatly
    toc: yes
    toc_depth: 5
    toc_float: yes
  pdf_document:
    toc: yes
    toc_depth: '5'
  html_document: 
    fig_caption: yes
    keep_md: yes
    toc: yes
    toc_depth: 5
    toc_float: yes
---

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- R generation

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x


# R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

[![What is R?](http://img.youtube.com/vi/XcBLEVknqvY/0.jpg)](http://www.youtube.com/watch?v=XcBLEVknqvY)


## R-project

https://cran.r-project.org/

---

[![](https://ismayc.github.io/talks/ness-infer/img/engine.png)](https://ismayc.github.io/talks/ness-infer/slide_deck.html#6)

---

## RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

---

[![](http://www-users.york.ac.uk/~er13/RStudio%20Anatomy.svg)](https://buzzrbeeline.blog/2018/07/04/rstudio-anatomy/)



---

### RStudio eklentileri

- Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/


```{r}
# devtools::install_github("rstudio/addinexamples", type = "source")
```


---

## X11

https://www.xquartz.org/

---

## Java OS

https://support.apple.com/kb/dl1572

---


# R zor şeyler için kolay, kolay şeyler için zor


- [R makes easy things hard, and hard things easy](http://r4stats.com/articles/why-r-is-hard-to-learn/)


- Aynı şeyi çok fazla şekilde yapmak mümkün

R Syntax Comparison::CHEAT SHEET

https://www.amelia.mn/Syntax-cheatsheet.pdf



---


# R paketleri


## Neden paketler var

[![](https://ismayc.github.io/talks/ness-infer/img/appstore.png)](https://ismayc.github.io/talks/ness-infer/slide_deck.html#7)

---

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">I love the <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> community.<br>Someone is like, &quot;oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It&#39;s called pbplyr.&quot;<br>What a tribe.</p>&mdash; Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) <a href="https://twitter.com/Frankly_Data/status/1014189095294291968?ref_src=twsrc%5Etfw">July 3, 2018</a></blockquote>

---



https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

---

![](https://blog.mitchelloharawild.com/blog/2018-07-11-user-2018-feature-wall_files/final.jpg)

---

## Paketleri nereden bulabiliriz

- Available CRAN Packages By Name  
https://cran.r-project.org/web/packages/available_packages_by_name.html

- CRAN Task Views  
https://cran.r-project.org/web/views/

- Bioconductor  
https://www.bioconductor.org

- RecommendR  
http://recommendr.info/

- pkgsearch  
CRAN package search  
https://github.com/metacran/pkgsearch

- CRANsearcher  
https://github.com/RhoInc/CRANsearcher  

- Awesome R  
https://awesome-r.com/  


## Kendi paket evrenini oluştur

- pkgverse: Build a Meta-Package Universe  
https://cran.r-project.org/web/packages/pkgverse/index.html



---

## R için yardım bulma


```{r yardım}
# ?mean
# ??efetch
# help(merge)
# example(merge)
```



- Vignette

![](figures/vignette.png)

---

- RDocumentation
https://www.rdocumentation.org

- R Package Documentation
https://rdrr.io/

- GitHub

- Stackoverflow

https://stackoverflow.com/

- Google uygun anahtar kelime



<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">How I use <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> <br>h/t <a href="https://twitter.com/ThePracticalDev?ref_src=twsrc%5Etfw">@ThePracticalDev</a> <a href="https://t.co/erRnTG0Ujr">pic.twitter.com/erRnTG0Ujr</a></p>&mdash; Emily Bovee (@ebovee09) <a href="https://twitter.com/ebovee09/status/1028037594947485696?ref_src=twsrc%5Etfw">August 10, 2018</a></blockquote>


---


![](figures/Google-package-name.png)

---



![](figures/Google-start-with-R.png)

---

- Awesome Cheatsheet
https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/


- Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/




- Twitter

https://twitter.com/hashtag/rstats?src=hash


- Reproducible Examples  

<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Got a question to ask on <a href="https://twitter.com/SlackHQ?ref_src=twsrc%5Etfw">@SlackHQ</a> or post on <a href="https://twitter.com/github?ref_src=twsrc%5Etfw">@github</a>? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by <a href="https://twitter.com/JennyBryan?ref_src=twsrc%5Etfw">@JennyBryan</a>) <a href="https://twitter.com/hashtag/rstat?src=hash&amp;ref_src=twsrc%5Etfw">#rstat</a> <a href="https://t.co/gpuGXpFIsX">pic.twitter.com/gpuGXpFIsX</a></p>&mdash; ZhiYang (@zhiiiyang) <a href="https://twitter.com/zhiiiyang/status/1053006003711569920?ref_src=twsrc%5Etfw">October 18, 2018</a></blockquote><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>


- Keeping up to date with R news  
https://masalmon.eu/2019/01/25/uptodate/  



---

## R paket yükleme

```
install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")
```

```{r paket yükleme}
# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")
```


```{r paket cagirma, error=FALSE, message = FALSE, warning = FALSE, eval = TRUE, include = TRUE}
# require(tidyverse)
# require(jmv)
# require(questionr)
# library(summarytools)
# library(gganimate)
```

---

# R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

![](http://www.rstudio.com/images/docs/projects_new.png)

---

# RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

![](https://support.rstudio.com/hc/en-us/article_attachments/206277618/data-import-overview.gif)

---

## Excel

## SPSS

## csv


---

# Veriyi görüntüleme

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Spreadsheet users using <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a>:  where&#39;s the data?<a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> users using spreadsheets:  where&#39;s the code?</p>&mdash; Leonard Kiefer (@lenkiefer) <a href="https://twitter.com/lenkiefer/status/1015587475580956672?ref_src=twsrc%5Etfw">July 7, 2018</a></blockquote>



```{r, results="markup"}
# library(nycflights13)
# summary(flights)
```



```
View(data)
```


```
data
```


```
head
```


```
tail
```


```
glimpse
```


```
str
```


```
skimr::skim()
```

---


# Veriyi değiştirme

## Veriyi kod ile değiştirelim

## Veriyi eklentilerle değiştirme

![](figures/change_data.png)


---


## RStudio aracılığıyla recode

*questionr* paketi kullanılacak

![](figures/level_recode.png)


---



https://juba.github.io/questionr/articles/recoding_addins.html


![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_1.png)


---

![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_2.png)


---

![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_3.png)


---

# Basit tanımlayıcı istatistikler

```
summary()
```

```
mean
```

```
median
```

```
min
```

```
max
```

```
sd
```

```
table()
```


```{r descriptive, echo=TRUE, include = TRUE}
library(readr)
irisdata <- read_csv("data/iris.csv")

jmv::descriptives(
    data = irisdata,
    vars = "Sepal.Length",
    splitBy = "Species",
    freq = TRUE,
    hist = TRUE,
    dens = TRUE,
    bar = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sum = TRUE,
    sd = TRUE,
    variance = TRUE,
    range = TRUE,
    se = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE,
    pcEqGr = TRUE)
```

---

```{r scatter, echo=TRUE, include=TRUE}
# install.packages("scatr")

scatr::scat(
    data = irisdata,
    x = "Sepal.Length",
    y = "Sepal.Width",
    group = "Species",
    marg = "dens",
    line = "linear",
    se = TRUE)

```

## summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
summarytools::freq(iris$Species, report.nas = FALSE, style = "rmarkdown", omit.headings = TRUE)
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
with(tobacco, print(ctable(smoker, diseased), method = 'render'))
```


```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
with(tobacco, 
     print(ctable(smoker, diseased, prop = 'n', totals = FALSE), 
           omit.headings = TRUE, method = "render"))
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
summarytools::descr(iris, style = "rmarkdown")
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
      omit.headings = TRUE, style = "rmarkdown")
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
# view(dfSummary(iris))

```


![](figures/dfsummary.png)



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
dfSummary(tobacco, plain.ascii = FALSE, style = "grid")
```


```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}

# First save the results

iris_stats_by_species <- by(data = iris, 
                            INDICES = iris$Species, 
                            FUN = descr, stats = c("mean", "sd", "min", "med", "max"), 
                            transpose = TRUE)

# Then use view(), like so:

view(iris_stats_by_species, method = "pander", style = "rmarkdown")
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
# view(iris_stats_by_species)
```

![](figures/DescriptiveStatistics.png)

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr, 
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr,  transpose = TRUE,
                      stats = c("mean", "sd", "min", "med", "max")))

view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)

# view(freq_tables, footnote = NA, file = 'freq-tables.html')
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
what.is(iris)
```

---

```{r}
freq(tobacco$gender, style = 'rmarkdown')
```

---

```{r}
print(freq(tobacco$gender), method = 'render')
```

---

## skimr

```
library(skimr)
skim(df)
```

---

## DataExplorer

```
library(DataExplorer)
DataExplorer::create_report(df)
```


[![](https://static1.squarespace.com/static/58eef8846a4963e429687a4d/t/5bdfc2fb4d7a9c04ee50b7aa/1541391160702/dataExplorerGifLg.gif?format=1500w)](https://www.littlemissdata.com/blog/simple-eda)



---

## Grafikler

```{r}
# library(ggplot2)
# library(mosaic)
# mPlot(irisdata)
```

---

```{r}
ctable(tobacco$gender, tobacco$smoker, style = 'rmarkdown')
```

---

```{r}
print(ctable(tobacco$gender, tobacco$smoker), method = 'render')
```

```
descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')
```


---



<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Here, building up a <a href="https://twitter.com/hashtag/ggplot2?src=hash&amp;ref_src=twsrc%5Etfw">#ggplot2</a> as slowly as possible, <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a>.  Incremental adjustments.  <a href="https://twitter.com/hashtag/rstatsteachingideas?src=hash&amp;ref_src=twsrc%5Etfw">#rstatsteachingideas</a> <a href="https://t.co/nUulQl8bPh">pic.twitter.com/nUulQl8bPh</a></p>&mdash; Gina Reynolds (@EvaMaeRey) <a href="https://twitter.com/EvaMaeRey/status/1029104656763572226?ref_src=twsrc%5Etfw">August 13, 2018</a></blockquote><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>


---


[![](https://raw.githubusercontent.com/dreamRs/esquisse/master/man/figures/esquisse.gif)](https://github.com/dreamRs/esquisse)


<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Dreaming of a fancy <a href="https://twitter.com/hashtag/Rstats?src=hash&amp;ref_src=twsrc%5Etfw">#Rstats</a> <a href="https://twitter.com/hashtag/ggplot?src=hash&amp;ref_src=twsrc%5Etfw">#ggplot</a> <a href="https://twitter.com/hashtag/dataviz?src=hash&amp;ref_src=twsrc%5Etfw">#dataviz</a> but still scared of typing <a href="https://twitter.com/hashtag/code?src=hash&amp;ref_src=twsrc%5Etfw">#code</a>? <a href="https://twitter.com/_pvictorr?ref_src=twsrc%5Etfw">@_pvictorr</a> esquisse package has you covered <a href="https://t.co/1vIDXcVAAF">https://t.co/1vIDXcVAAF</a> <a href="https://t.co/RlTkptnrNv">pic.twitter.com/RlTkptnrNv</a></p>&mdash; Radoslaw Panczak (@RPanczak) <a href="https://twitter.com/RPanczak/status/1047019588658040832?ref_src=twsrc%5Etfw">October 2, 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>










---

# Rcmdr

```
library(Rcmdr)

Rcmdr::Commander()

```


- A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/


---

# jamovi

https://www.jamovi.org/

![![](https://www.jamovi.org/assets/main-screenshot.png)](https://www.jamovi.org/)


https://blog.jamovi.org/2018/07/30/rj.html

![![](https://blog.jamovi.org/assets/images/rj.png)](https://blog.jamovi.org/2018/07/30/rj.html)

---

# Sonraki Konular

- RStudio ile GitHub
- Hipotez testleri
- R Markdown ve R Notebook ile tekrarlanabilir rapor


---

# Diğer kodlar

- Diğer kodlar için bakınız: [https://sbalci.github.io/](https://sbalci.github.io/)


---

# Geri Bildirim

- Geri bildirim için tıklayınız: _[Geri bildirim formu](https://goo.gl/forms/YjGZ5DHgtPlR1RnB3)_


---

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